Abstract | ||
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There has been a recent surge in work in probabilistic databases, propelled in large part by the huge increase in noisy data sources -- sensor data, experimental data, data from uncurated sources, and many others. There is a grow- ing need to be able to flexibly represent the uncertainties in the data, and to efficiently query the data. Building on existing probabilistic database work, we present a unifying framework which allows a flexible representation of corre- lated tuple and attribute level uncertainties. An important capability of our representation is the ability to represent shared correlation structures in the data. We provide moti- vating examples to illustrate when such shared correlation structures are likely to exist. Representing shared corre- lations structures allows the use of sophisticated inference techniques based on lifted probabilistic inference that, in turn, allows us to achieve significant speedups while com- puting probabilities for results of user-submitted queries. |
Year | DOI | Venue |
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2007 | 10.1109/ICDMW.2007.82 | ICDM Workshops |
Keywords | Field | DocType |
sophisticated inference technique,correlation structure,probabilistic databases,probabilistic database work,noisy data source,shared correlation structure,flexible representation,probabilistic inference,attribute uncertainty,sensor data,experimental data,representing tuple,probabilistic database,database management systems | Data mining,Noisy data,Experimental data,Computer science,Artificial intelligence,Probabilistic logic,Probabilistic inference,Tuple,Inference,Probabilistic relevance model,Database,Machine learning,Probabilistic database | Conference |
ISBN | Citations | PageRank |
0-7695-3033-8 | 15 | 0.84 |
References | Authors | |
22 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Prithviraj Sen | 1 | 837 | 38.24 |
Amol Deshpande | 2 | 4085 | 258.89 |
Lise Getoor | 3 | 4365 | 320.21 |